Learning method, state estimation method, and state estimation device for state estimation model of secondary battery

ABSTRACT

A learning method of a state estimation model of a secondary battery includes training the state estimation model to learn a relationship of a state estimation input data preprocessed from state variables including measured terminal currents and terminal voltages of the secondary battery with a charge rate or a deterioration degree of the secondary battery. The state estimation input data includes time-series data of difference gradients, which is a change rate of the differences of the terminal voltages with respect to the differences of the terminal currents.

INCORPORATION BY REFERENCE

The present application claims priority under 35 U.S.C. § 119 toJapanese Patent Application No. 2021-036376 filed on Mar. 8, 2021. Thecontent of the application is incorporated herein by reference in itsentirety.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to a learning method, a state estimationmethod, and a state estimation device, of a state estimation model forestimating the state of an operating secondary battery.

Description of the Related Art

Secondary batteries, which are storage batteries that can be usedrepeatedly by charging, are widely used in moving objects such aselectric vehicles and electric bicycles, and buildings. When thesesecondary batteries are used, it is important to appropriately know thestate of the secondary batteries for the purpose of knowing appropriatecharging timing and replacement timing. Here, the state of the secondarybattery means SOC (charge rate, remaining capacity, State Of Charge)and/or SOH (deterioration degree, State Of Health).

Conventionally, it is known to use two neural networks for the purposeof appropriately automatically determining the deterioration state andSOC of an operating secondary battery in real time (Japanese PatentLaid-Open No. 2003-249271). This technique uses: a first neural networktrained to estimate the deterioration state D (distinction of “normal”,“caution” and “deterioration”) of the secondary battery, from thetime-series combination of the measured values of the operatingparameters (voltage V, current I, internal impedance Z, temperature T)of the secondary battery; and a second neural network trained toestimate the SOC of the secondary battery from the measured values ofthe operating parameters and the estimated deterioration state D.

On the other hand, electrical characteristics of secondary batteriesincluding, for example, SOC-OCV (open circuit voltage) characteristics,internal impedance characteristics, and/or dependence of thesecharacteristics on SOH, may vary depending on the manufacturers and/ormodels of the secondary batteries. Therefore, the relationship of thevoltage V, current I, and internal impedance Z with the SOC and/or SOH(hereinafter, SOC and the like), of the secondary battery, often greatlyvaries depending on the manufacturer and models of the secondarybattery.

Therefore, the above-mentioned conventional technique, which directlyinput the measured values of the voltage V, the current I, the internalimpedance Z, and the temperature of the secondary battery to the neuralnetwork in training the neural network, determines one manufacturer andmodel of the secondary battery (target secondary battery) to beestimated, and uses a secondary battery of the same manufacturer andmodel to collect the training data of the neural network.

The conventional neural network trained in this way can accuratelyestimate the SOC and the like for a secondary battery of the samemanufacturer and model as the secondary battery to be estimated.However, when secondary batteries with different electricalcharacteristics of various manufacturers and/or models are to beestimated, the conventional neural network has difficulty in accuratelyestimating those SOCs and the like.

However, in estimating the state of an operating secondary battery in avehicle, for example, if one or a set of estimation models (for example,a neural network) can commonly be used to accurately estimate the SOCsand the like of secondary batteries of various manufacturers and models,the estimation models would conveniently expand the range of choices forthe secondary battery to be used.

The present invention has been made in view of the above-mentionedcircumstances, and an object of the present invention is to accuratelyestimate charge rates (SOC) and/or deterioration degrees (SOH) ofoperating secondary batteries with various electrical characteristics ofdifferent manufacturers and models of these secondary batteries.

SUMMARY OF THE INVENTION

An aspect of the present invention is

a learning method of a state estimation model of a secondary battery,the learning method using machine learning, the state estimation modelestimating a charge rate and/or a deterioration degree of the operatingsecondary battery, the secondary battery being connected to a load or acharger, the method including:

a step of measuring state variables at predetermined time intervals, thestate variables including terminal currents and terminal voltages of theoperating secondary battery;

a step of calculating state estimation input data by preprocessing thestate variables; and

a step of training the state estimation model to learn a relationship ofthe state estimation input data with the charge rate and/or thedeterioration degree of the operating secondary battery, by machinelearning,

wherein the step of calculating:

-   -   uses time-series data of the terminal currents and time-series        data of the terminal voltages to calculate current differences        and voltage differences, each current difference being a        difference in the terminal currents, each voltage difference        being a difference in the terminal voltages;    -   uses time-series data of the current differences and time-series        data of the voltage differences to calculate difference        gradients, each difference gradient being a change rate of the        voltage differences with respect to the current differences in a        period to a present from a past that goes back a first        predetermined time from the present; and    -   generates the state estimation input data including time-series        data of the difference gradients.

According to another aspect of the present invention,

-   -   the state estimation input data further includes        -   time-series data of open circuit voltages of the operating            secondary battery,        -   time-series data of first gradient change rates, and        -   time-series data of second gradient change rates,    -   each first gradient change rate being determined as follows:        -   an integrated current value is determined to be a sum of the            terminal current values acquired continuously in a period to            a present from a past that goes back a second predetermined            time from the present;        -   a difference gradient change amount is determined by            subtracting the difference gradient in the past that goes            back the second predetermined time from the present, from            the present difference gradient;        -   time-series data of the integrated current values and            time-series data of the difference gradient change amounts            determines a change rate of the difference gradient change            amounts with respect to the integrated current values; and        -   the first gradient change rate is determined to be the            change rate of the difference gradient change amounts with            respect to the integrated current values, in a period to the            present from a past that goes back a third predetermined            time from the present,    -   each second gradient change rate being determined as follows:        -   an open circuit voltage change amount is determined by            subtracting the open circuit voltage in the past that goes            back the second predetermined time from the present, from            the present open circuit voltage;        -   time-series data of the open circuit voltage change amounts            and time-series data of the difference gradient change            amounts determines a change rate of the difference gradient            change amounts with respect to the open circuit voltage            change amounts; and        -   the second gradient change rate is determined to be the            change rate of the difference gradient change amounts with            respect to the open circuit voltage change amounts, in the            period to the present from the past that goes back the third            predetermined time from the present.

According to yet another aspect of the present invention,

each first gradient change rate and each second gradient change rate arecalculated using the least squares method.

According to yet another aspect of the present invention,

the step of calculating:

-   -   uses time-series data of the terminal currents and time-series        data of the terminal voltages, and time-series data of the        difference gradients, as voltage estimation input data, to        estimate an open circuit voltage of the operating secondary        battery; and    -   uses the estimated open circuit voltage to calculate the state        estimation input data.

According to yet another aspect of the present invention,

the step of calculating uses the open circuit voltage estimated using atrained open circuit voltage estimation model, and thereby calculatesthe state estimation input data, the trained open circuit voltageestimation model having learned a relationship between the voltageestimation input data and an open circuit voltage of the operatingsecondary battery.

According to yet another aspect of the present invention,

each difference gradient is calculated using the least squares method.

According to yet another aspect of the present invention,

each current difference and each voltage difference are respectively afourth-order difference of time-series data of the terminal currents anda fourth-order difference of time-series data of the terminal voltages.

According to yet another aspect of the present invention,

the state estimation model is configured of an RNN (Recurrent NeuralNetwork).

According to yet another aspect of the present invention,

an intermediate layer of the RNN configuring the state estimation modelis configured of an LSTM (Long Short Term Memory) or a GRU (GatedRecurrent Unit).

According to yet another aspect of the present invention,

the state estimation model is configured of a one-dimensional CNN(Convolutional Neural Network).

According to yet another aspect of the present invention,

the state estimation model is generated by learning using time-seriesdata of state variables including terminal currents and terminalvoltages for each of a plurality of secondary batteries with differentelectrical characteristics, the secondary batteries each being connectedto a load or a charger.

Yet another aspect of the present invention is

a state estimation method of a secondary battery, the method including:

a step of measuring state variables at predetermined time intervals, thestate variables including terminal currents and terminal voltages of theoperating secondary battery, the secondary battery being connected to aload or a charger;

a step of calculating state estimation input data by preprocessing thestate variables; and

a step of estimating a present charge rate and/or a presentdeterioration degree of the operating secondary battery, from the stateestimation input data, using the state estimation model trained by alearning method of the state estimation model of the secondary batteryaccording to any one of the above;

wherein the step of calculating:

-   -   uses time-series data of the terminal currents and time-series        data of the terminal voltages to calculate current differences        and voltage differences, each current difference being a        difference in the terminal currents, each voltage difference        being a difference in the terminal voltages;    -   uses time-series data of the current differences and time-series        data of the voltage differences to calculate difference        gradients, each difference gradient being a change rate of the        voltage differences with respect to the current differences in a        period to a present from a past that goes back a first        predetermined time from the present; and    -   generates the state estimation input data including time-series        data of the difference gradients.

Yet another aspect of the present invention is

a state estimation device of a secondary battery, the device including aprocessor,

wherein the processor is configured to:

-   -   measure state variables at predetermined time intervals, the        state variables including terminal currents and terminal        voltages of an operating secondary battery;    -   perform preprocessing of the measured state variables, to        calculate state estimation input data; and    -   estimate a present charge rate and/or a present deterioration        degree of the operating secondary battery, from the state        estimation input data, using a state estimation model trained by        the learning method of the state estimation model of the        secondary battery according to any one of the above,

wherein in the preprocessing, the processor:

-   -   uses time-series data of the measured terminal currents and        time-series data of the measured terminal voltages, to calculate        current differences and voltage differences, each current        difference being a difference in the terminal currents, each        voltage difference being a difference in the terminal voltages;    -   uses time-series data of the current differences and time-series        data of the voltage differences to calculate difference        gradients, each difference gradient being a change rate of the        voltage differences with respect to the current differences in a        period to a present from a past that goes back a first        predetermined time from the present; and    -   generates the state estimation input data including time-series        data of the difference gradients.

According to an aspect of the present invention, the charge rates (SOC)and/or deterioration degrees (SOH) of secondary batteries with variouselectrical characteristics of different manufacturers and models can beaccurately estimated in operation of these secondary batteries.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart showing a procedure of a learning method of astate estimation model according to a first embodiment of the presentinvention;

FIG. 2 is a diagram showing a configuration of a machine learning devicethat executes the learning method of the state estimation model shown inFIG. 1;

FIG. 3 is a flow chart showing details of processing in a step ofcalculating state estimation input data in the learning method of thestate estimation model shown in FIG. 1;

FIG. 4 is a diagram for describing a calculation of a current differencein the processes shown in FIG. 3;

FIG. 5 is a diagram for describing a calculation of a voltage differencein the processes shown in FIG. 3;

FIG. 6 is a diagram for describing a calculation of a differencegradient in the processes shown in FIG. 3;

FIG. 7 is a diagram for describing calculations of an integrated currentvalue, an open circuit voltage change, and a difference gradient changeamount in the processes shown in FIG. 3;

FIG. 8 is a diagram for describing a calculation of a first gradientchange rate in the processes shown in FIG. 3;

FIG. 9 is a diagram for describing a calculation of a second gradientchange rate in the processes shown in FIG. 3;

FIG. 10 is a diagram showing an example of the configuration of an opencircuit voltage estimation model generated by a model learning unit ofthe machine learning device shown in FIG. 2;

FIG. 11 is a diagram showing an example of the configuration of thestate estimation model generated by the model learning unit of themachine learning device shown in FIG. 2;

FIG. 12 is a diagram showing an example of a state estimation of asecondary battery performed using a trained open circuit voltageestimation model and a trained state estimation model;

FIG. 13 is a flow chart showing a procedure of a state estimation methodaccording to a second embodiment of the present invention;

FIG. 14 is a diagram showing a configuration of a state estimationdevice that executes the state estimation method shown in FIG. 13; and

FIG. 15 is a functional block diagram of a processing device included inthe state estimation device shown in FIG. 14.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The inventor of the invention of the present application has found thatthere is a correlation between: a change mode of terminal current andterminal voltage changes of secondary batteries, that is, a high-orderchange mode; and an internal state (OCV, SOC and or SOH) of thesecondary batteries, at least among the secondary batteries in the sametype (for example, the secondary batteries in “lithium ion batteries”that are identical as the type). Then, the inventor has obtainedknowledge such that: a parameter expressing a high-order change mode ofterminal current and terminal voltage of the secondary battery to beused is determined to be a change rate (difference gradient to bedescribed below) of the differences in time-series data of the terminalvoltages (voltage differences) with respect to the differences intime-series data of the terminal currents (current differences); thisparameter is determined to be an input for a model (for example, aneural network); and thereby there can be generated a model that canaccurately estimate a state of secondary batteries with variouselectrical characteristics of different manufacturers and models. Theinvention of the present application is based on such outstandingknowledge.

The following describes embodiments of the present invention withreference to the drawings.

First Embodiment

FIG. 1 is a diagram showing a procedure of a learning method of a stateestimation model of a secondary battery according to a first embodimentof the present invention. The learning method of this state estimationmodel includes: a step (S100) of measuring a state variable including aterminal current and a terminal voltage of an operating secondarybattery to which a load or a charger is connected, at predetermined timeintervals; and a step (S102) of preprocessing the measured statevariable to calculate the state estimation input data. In addition, thislearning method of an open circuit voltage estimation model includes: astep (S104) of training the state estimation model by machine learningto learn a relationship of the state estimation input data with thecharge rate and/or deterioration degree as a state of the operatingsecondary battery.

FIG. 2 is a diagram showing an example of the configuration of alearning management device and a machine learning device that executethe learning method of the state estimation model shown in FIG. 1. Thelearning management device and the machine learning device also performmachine learning of the open circuit voltage estimation model forestimating the open circuit voltage of the secondary battery. The stateestimation model and the open circuit voltage estimation model areconfigured of, for example, a neural network. A learning managementdevice 112 controls the operation of a secondary battery 102 during themachine learning, calculates the measured values of the open circuitvoltage, SOC (state of charge), and SOH (state of health) as teacherdata, and provides the machine learning device 100 with the measuredvalues.

The secondary battery 102 is charged by the charger 104 and dischargedby energizing the load 106. The charger 104 is, for example, a DC powersource, and the load 106 is, for example, a motor. Whether to charge thesecondary battery 102 from the charger 104 or discharge it to the load106 is chosen by the changeover switch 108. The changeover switch 108and the secondary battery 102 have a characteristic measuring instrument110 inserted therebetween.

The characteristic measuring instrument 110 measures the present valuesof predetermined state variables of the secondary battery 102. Thepredetermined state variables may include a terminal voltage Vte, aterminal current Ite, an internal impedance Z of the secondary battery102, and a temperature T (° C.) of the housing surface of the secondarybattery 102. Here, the internal impedance Z can be measured according tothe prior art, for example, by inputting an alternating current, whichis a measurement signal, to the secondary battery 102.

The terminal current Ite of the secondary battery 102 takes a positivevalue when the secondary battery 102 discharges and takes a negativevalue when it charges.

[1. Learning Management Device]

The learning management device 112 controls charge-discharge of thesecondary battery 102, generates teacher data for training the opencircuit voltage estimation model and the state estimation model, andoutputs the teacher data to the machine learning device 100. Thelearning management device 112 is, for example, a computer, which startsan operation according to an instruction from an operator, and gives aninstruction to start and stop the power output to the charger 104 and aninstruction to change the operation to the changeover switch 108.

The learning management device 112 acquires a terminal current Ite, aterminal voltage Vte, and an internal impedance Z of the secondarybattery 102 during charging and discharging from the characteristicmeasuring instrument 110 at predetermined time intervals.

The learning management device 112 calculates the open circuit voltageVoc of the secondary battery 102 from the acquired terminal current Ite,terminal voltage Vte, and internal impedance Z, and generatestime-series data of the open circuit voltages Voc. The time-series dataof the open circuit voltages Voc is used as teacher data at the time oftraining the open circuit voltage estimation model executed by themachine learning device 100 to be described below.

In addition, the learning management device 112 uses the time-seriesdata of the terminal voltages Vte and terminal currents Ite acquiredabove to calculate the charge amount (full charge amount) when thesecondary battery 102 charges to the limit and the present chargeamount. In this embodiment, SOH is the full charge amount (unit: Ah),and SOC is the ratio (%) of the present charge amount to the full chargeamount.

[2. Machine Learning Device]

The machine learning device 100 includes a processing device 120 and astorage device 122. The storage device 122 is composed of, for example,a volatile and/or non-volatile semiconductor memory, a hard disk device,or the like. The storage device 122 stores an open circuit voltageestimation model 124 and the state estimation model 126 generated by amodel learning unit 134 to be described below.

The processing device 120 is, for example, a computer including aprocessor such as a CPU (Central Processing Unit). The processing device120 may have a configuration including a ROM (Read Only Memory) in whicha program is written, a RAM (Random Access Memory) for temporarilystoring data. The processing device 120 includes a state variablemeasuring unit 130, an input data generation unit 132, and a modellearning unit 134, which serve as functional elements or functionalunits.

These functional elements included in the processing device 120 areembodied, for example, by the processing device 120, which is acomputer, executing a program. Note that the computer program can bestored in any computer-readable storage medium. Alternatively, all orpart of the functional elements included in the processing device 120may be configured by hardware including one or more electronic circuitcomponents.

[2.1. Function of State Variable Measuring Unit]

The state variable measuring unit 130 executes the step S100 shown inFIG. 1. Specifically, the state variable measuring unit 130 acquires astate variable including the terminal current Ite and the terminalvoltage Vte of the secondary battery 102 to which the load 106 or thecharger 104 is connected, from the characteristic measuring instrument110 at predetermined time intervals. Thus, the state variable measuringunit 130 measures the state variables at predetermined time intervals.The state variable measuring unit 130 may further measure thetemperature T of the secondary battery 102, which is a state variable,at the predetermined time intervals.

[2.2. Functions of Input Data Generation Unit]

The input data generation unit 132 uses the terminal currents Ite andterminal voltages Vte acquired by the state variable measuring unit 130,to generate voltage estimation input data for training the open circuitvoltage estimation model 124. Furthermore, for example, after the inputdata generation unit 132 finishes training the open circuit voltageestimation model 124, the input data generation unit 132 executes thestep S102 shown in FIG. 1 and also uses the trained open circuit voltageestimation model 124, to generate state estimation input data fortraining the state estimation model 126.

[2.2.1. Generation of Voltage Estimation Input Data]

The voltage estimation input data is input data to the open circuitvoltage estimation model 124 generated by the input data generation unit132 for training the open circuit voltage estimation model 124.

Specifically, the input data generation unit 132 first calculates acurrent difference δIte, which is the difference of the terminal currentIte, and a voltage difference δVte, which is the difference of theterminal voltage Vte from the time-series data of the terminal currentsIte and the time-series data of the terminal voltages Vte. In thisembodiment, the current difference δIte and the voltage difference δVteare respectively the fourth-order differences Δ⁴Ite of the time-seriesdata of the terminal currents Ite and the fourth-order differences Δ⁴Vteof the time-series data of the terminal voltages Vte.

The input data generation unit 132 uses the time-series data of thecurrent differences δIte and the time-series data of the voltagedifferences δVte calculated above, to calculate the difference gradientSdiff, which is the change rate of the voltage difference δVte withrespect to the current difference δIte, in a period to the present froma past that goes back a predetermined time T1 from the present.

The calculation of the current difference δIte, the voltage differenceδVte, and the difference gradient Sdiff is to be described in detail inthe description of the state estimation input data to be describedbelow.

Then, the input data generation unit 132 generates voltage estimationinput data including at least the following three time-series data,which are input data for training the open circuit voltage estimationmodel 124, in a period to the present from a past that goes back apredetermined time T2 from the present:

time-series data of terminal currents Ite,

time-series data of terminal voltages Vte, and

time-series data of the difference gradients Sdiff.

[2.2.2. Generation of State Estimation Input Data]

The state estimation input data is input data to the state estimationmodel 126 generated by the input data generation unit 132 in the stepS102 of the learning method shown in FIG. 1. Note that the stateestimation input data in the step S102 is generated after the opencircuit voltage estimation model finishes learning.

FIG. 3 is a flow chart showing details of the processing in the stepS102 of calculating the state estimation input data in FIG. 1. In thestep S102 of calculating the state estimation input data, the input datageneration unit 132 first calculates the current differences δIte andthe voltage differences δVte, from the time-series data of the terminalcurrents Ite and the time-series data of the terminal voltages Vte. Eachcurrent difference δIte is the difference in the terminal currents Ite,and each voltage difference δVte is the difference in the terminalvoltages Vte (S200). Then, the input data generation unit 132 calculatesthe difference gradient Sdiff (S202) in the period to the present fromthe past that goes back the predetermined time T1 from the present. Thedifference gradient Sdiff is the change rate of the voltage differenceδVte with respect to the current difference δIte.

The input data generation unit 132 inputs the time-series data of theterminal currents Ite, terminal voltages Vte, and the differencegradients Sdiff in the period to the present from the past that goesback a predetermined time T2 from the present, into the open circuitvoltage estimation model 430, and thereby estimates the present opencircuit voltage Voc (S204). Subsequently, the input data generation unit132 subtracts the difference gradient Sdiff in a past that goes back apredetermined time T3 from the present, from the present differencegradient Sdiff, and calculates a difference gradient change amount Dsd(S206).

Then, the input data generation unit 132 calculates an integratedcurrent value ΣIte, which is the sum of the terminal currents Itemeasured in the period to the present from the past that goes back thepredetermined time T3 from the present (S208). Then, the input datageneration unit 132 calculates a change rate of the difference gradientchange amounts Dsd with respect to the integrated current values ΣIte ina period to the present from a past that goes back a predetermined timeT4 from the present. The change rate of the difference gradient changeamount Dsd is a first gradient change rate R1 (S210).

Furthermore, the input data generation unit 132 subtracts the opencircuit voltage Voc in the past that goes back the predetermined time T3from the present, from the present open circuit voltage Voc, and therebycalculates an open circuit voltage change amount Dvoc (S212). Then, theinput data generation unit 132 calculates a change rate of thedifference gradient change amount Dsd with respect to the open circuitvoltage change amount Dvoc in the period to the present from the pastthat goes back the predetermined time T4 from the present. The changerate of the difference gradient change amount Dsd is a second gradientchange rate R2 (S214).

Then, the input data generation unit 132 generates the state estimationinput data including, for example, the following four time-series data(S216) in a period to the present from the past that goes back apredetermined time T5 from the present, and ends the processing:

time-series data of the difference gradients Sdiff.

the time-series data of open circuit voltages Voc,

the time-series data of the first gradient change rates R1, and

the time-series data of the second gradient change rates R2.

The following describes: a specific method of calculating the currentdifference δIte, the voltage difference δVte, the difference gradientSdiff, the integrated current value ΣIte, the open circuit voltagechange amount Dvoc, the difference gradient change amount Dsd, the firstgradient change rate R1, and the second gradient change rate R2; and thevoltage estimation input data and the state estimation input data.

[2.2.2.1. Calculation of Current Difference δIte]

FIG. 4 is a diagram for explaining the calculation of the currentdifference δIte. In the table shown in FIG. 4, the leftmost column isthe first column, and toward the right, there are the second column, thethird column, and finally the sixth column. The first column of thetable of FIG. 4 indicates the time when the state variable measuringunit 130 repeatedly acquires the terminal current Ite at the timeinterval dt or the index (number) of the time. The second column is thetime-series data of the terminal currents Ite, and indicates theterminal current Ite acquired at each time.

The third, fourth, fifth, and sixth columns respectively indicate thefirst-order difference Δ¹Ite, the second-order difference Δ²Ite, thethird-order difference Δ³Ite, and the fourth-order difference Δ⁴Ite ofthe terminal current Ite, which are calculated from the terminal currentIte in the second row.

The hth-order difference Δ^(h)Ite(t_(n)) (h=1, 2, . . . 4) at thepresent time t_(n) is calculated by the following expression.

Δ^(h)Ite(t _(n))=Δ^(h−1)Ite(t _(n))−Δ^(h−1)Ite(t _(n−1))

where h=1, 2, 3, 4. In addition, it is assumed thatΔ⁰Ite(t_(n))=Ite(t_(n)).

In other words, the first-order difference Δ¹Ite(t_(n)) at the timet_(n) is calculated by subtracting the terminal current Ite(t_(n−1)) attime t_(n−1) from the terminal current Ite(t_(n)) at the time t_(n).Furthermore, the second-order difference Δ²Ite(t_(n)) at the time t_(n)is calculated by subtracting the first-order difference Δ¹Ite(t_(n−1))at the time t_(n−1) from the first-order difference Δ¹Ite(t_(n)) at thetime t_(n).

Similarly, the third-order difference Δ³Ite(t_(n)) at the time t_(n) iscalculated by subtracting the second-order difference Δ²Ite(t_(n−1)) atthe time t_(n−1) from the second-order difference Δ²Ite(t_(n)) at thetime t_(n). Similarly, the fourth-order difference Δ⁴Ite(t_(n)) at thetime t_(n) is calculated by subtracting the third-order differenceΔ³Ite(t_(n−1)) at the time t_(n−1) from the third-order differenceΔ³Ite(t_(n)) at the time t_(n).

In this embodiment, the input data generation unit 132 determines thefourth-order difference Δ⁴Ite of the terminal current Ite at each timeto be the current difference δIte. In other words,

δIte(t)=Δ⁴Ite(t), where t=t _(n), . . . .

[2.2.2.2. Calculation of Voltage Difference δVte]

The input data generation unit 132 calculates the voltage differenceδVte of the terminal voltage Vte in the same manner as the currentdifference described above. FIG. 5 is a diagram showing a procedure forcalculating the voltage difference δVte. In the table shown in FIG. 5,the leftmost column is the first column, and toward the right, there arethe second column, the third column, and finally the sixth column. Thefirst column of the table of FIG. 5 indicates the time when the statevariable measuring unit 130 repeatedly acquires the terminal voltage Vteat the time interval dt or the index (number) of the time. The secondcolumn is the time-series data of the terminal voltages Vte, andindicates the terminal voltage Vte acquired at each time.

The third, fourth, fifth, and sixth columns respectively indicate thefirst-order difference Δ¹Vte, the second-order difference Δ²Vte, thethird-order difference Δ³Vte, and the fourth-order difference Δ⁴Vte ofthe terminal voltage Vte, which are calculated from the terminal voltageVte in the second column.

The hth-order difference Δ^(h)Vte (t_(n)) (h=1, 2, . . . 4) at thepresent time t_(n) is calculated by the following expression.

Δ^(h)Vte(t _(n))=Δ^(h−1)Vte(t _(n))−Δ^(h−1)Vte(t _(n−1))

where h=1, 2, 3, 4.

In addition, it is assumed that Δ⁰Vte(t_(n))=Vte(t_(n)).

In other words, the first-order difference Δ¹Vte(t_(n)) at the timet_(n) is calculated by subtracting the terminal voltage Vte(t_(n−1)) atthe time t_(n−1) from the terminal voltage Vte(t_(n)) at the time t_(n).In addition, the second-order difference Δ²Vte(t_(n)) at the time t_(n)is calculated by subtracting the first-order difference Δ¹Vte(t_(n−1))at the time t_(n−1) from the first-order difference Δ¹Vte(t_(n)) at thetime t_(n).

Similarly, the third-order difference Δ³Vte(t_(n)) at the time t_(n) iscalculated by subtracting the second-order difference Δ²Vte(t_(n−1)) atthe time t_(n−1) from the second-order difference Δ²Vte(t_(n)) at thetime t_(n). Similarly, the fourth-order difference Δ⁴Vte(t_(n)) at thetime t_(n) is calculated by subtracting the third-order differenceΔ³Vte(t_(n−1)) at the time t_(n−1) from the third-order differenceΔ³Vte(t_(n)) at the time t_(n).

In this embodiment, the input data generation unit 132 determines thefourth-order difference Δ⁴Vte of the terminal voltage Vte at each timeto be the voltage difference δVte. In other words,

δVte(t)=Δ⁴Vte(t)

where t=t_(n), t_(n−1), . . . .

[2.2.2.3. Calculation of Difference Gradient Sdiff]

The difference gradient Sdiff is the change rate of the voltagedifference δVte with respect to the current difference δIte in theperiod to the present from the past that goes back the predeterminedtime T1 from the present. Specifically, as shown in FIGS. 4 and 5, theinput data generation unit 132 extracts k1 (k1=n−m+1) of currentdifferences δIte and k1 of voltage differences δVte between the pasttime t_(m) and the present time t_(n), which corresponds to the periodto the present from the past that goes back the predetermined time T1from the present. Then, the input data generation unit 132 uses the dataset (δIte, δVte) each time configured of the extracted δIte and δVte tocalculates the difference gradient Sdiff by the least squares method.The difference gradient Sdiff is the change rate of the voltagedifference δVte with respect to the current difference δIte.

More specifically, as shown in FIG. 6, the above k1 of data sets (δIte,δVte) are plotted on a two-dimensional plane having the currentdifference δIte as the horizontal axis and the voltage difference δVteas the vertical axis. The plot is represented by a black circle in theillustrated dotted ellipse. The slope of the approximate straight line(regression straight line) 200 of this plot corresponds to thedifference gradient Sdiff. In other words, when the approximate straightline 200 is given by δVte=a1×δIte+b1, the slope a1 of the approximatestraight line 200 corresponds to the difference gradient Sdiff. Here,the approximate straight line is calculated by, for example, the leastsquares method.

[2.2.2.4. Calculation of Integrated Current Value ΣIte, Open CircuitVoltage Change Amount Dvoc, and Difference Gradient Change Amount Dsd]

The following describes the calculation of the integrated current valueΣIte, the open circuit voltage change amount Dvoc, and the differencegradient change amount Dsd.

FIG. 7 is a diagram for explaining the calculation of the integratedcurrent value ΣIte, the open circuit voltage change amount Dvoc, and thedifference gradient change amount Dsd. In the table shown in FIG. 7, theleftmost column is the first column, and toward the right, there are thesecond column, the third column, and finally the seventh column. Thefirst column of the table of FIG. 7 indicates the time when the statevariable measuring unit 130 repeatedly acquires the terminal current Iteat the time interval dt or the index (number) of the time. The secondcolumn is the time-series data of the terminal currents Ite, andindicates the terminal current Ite acquired at each time. The thirdcolumn indicates the integrated current values ΣIte calculated from thetime-series data of the terminal currents Ite in the second column.

The fourth column is the time-series data of the open circuit voltagesVoc, and indicates the open circuit voltage Voc acquired at each time.In this embodiment, the open circuit voltage Voc is calculated andacquired using the trained open circuit voltage estimation model 124.The fifth column indicates the open circuit voltage change amounts Dvoccalculated from the time-series data of the open circuit voltages Voc inthe fourth column.

The sixth column indicates the difference gradients Sdiff calculated asdescribed above, and the seventh column indicates the differencegradient change amounts Dsd calculated from the time-series data of thedifference gradients Sdiff in the sixth column.

The integrated current value ΣIte(t_(n)) at the present time t_(n) isthe sum of the terminal currents Ite measured in the period to thepresent time t_(n) from time t_(p) in the past that goes back thepredetermined time T3 from the present, and is calculated by thefollowing expression.

$\begin{matrix}\left\lbrack {{Expression}1} \right\rbrack & \end{matrix}$${\Sigma{Ite}\left( t_{n} \right)} = {{{{Ite}\left( t_{p} \right)} + {{Ite}\left( t_{p + 1} \right)} + \ldots + {{Ite}\left( t_{n - 1} \right)} + {{Ite}\left( t_{n} \right)}} = {\sum\limits_{t_{i} = t_{p}}^{t_{n}}{{Ite}\left( t_{i} \right)}}}$

The open circuit voltage change amount Dvoc(t_(n)) at the present timet_(n) is a value obtained by subtracting the open circuit voltageVoc(t_(p)) at time t_(p) in the past that goes back the predeterminedtime T3 from the present, from the open circuit voltage Voc(t_(n)) atthe present time t_(n), and is calculated by the following expression.

Dvoc(t _(n))=Voc(t _(n))−Voc(t _(p))

The difference gradient change amount Dsd(t_(n)) at the present timet_(n) is a value obtained by subtracting the difference gradientSdiff(t_(p)) at time t_(p) in the past that goes back the predeterminedtime T3 from the present, from the difference gradient Sdiff(t_(n)) atthe present time t_(n), and is calculated by the following expression.

Dsd(t _(n))=Sdiff(t _(n))−Sdiff(t _(p))

[2.2.2.5. Calculation of First Gradient Change Rate R1 And SecondGradient Change Rate R2]

The following describes the calculation of the first gradient changerate R1 and the second gradient change rate R2.

The first gradient change rate R1 is the change rate of the differencegradient change amounts Dsd with respect to the integrated currentvalues ΣIte in the period to the present from the past that goes backthe predetermined time T4 from the present. Specifically, as shown inFIG. 7, the input data generation unit 132 extracts k2 (k2=n−q+1) of theintegrated current value ΣIte and k2 of the difference gradient changeamount Dsd, between the past time t_(q) and the present time t_(n),which corresponds to the period to the present from the past that goesback the predetermined time T4 from the present. Then, the input datageneration unit 132 uses the extracted ΣIte and Dsd data sets (ΣIte,Dsd) each time to calculate the first gradient change rate R1, which isthe change rate of the difference gradient change amounts Dsd withrespect to the integrated current values ΣIte, by the least squaresmethod.

More specifically, as shown in FIG. 8, the above k2 of data sets (ΣIte,Dsd) are plotted on a two-dimensional plane having the integratedcurrent value ΣIte as the horizontal axis and the difference gradientchange amount Dsd as the vertical axis. The plot is represented by ablack circle in the illustrated dotted ellipse. The slope of theapproximate straight line(regression straight line) 202 of the plotcorresponds to the first gradient change rate R1. In other words, whenthe approximate straight line 202 is given by Dsd=a2×ΣIte+b2, the slopea2 of the approximate straight line 202 corresponds to the firstgradient change rate R1. Here, the approximate straight line iscalculated by, for example, the least squares method.

The second gradient change rate R2 is the change rate of the differencegradient change amount Dsd with respect to the open circuit voltagechange amount Dvoc, in the period to the present from the past that goesback the predetermined time T4 from the present. Specifically, as shownin FIG. 7, the input data generation unit 132 extracts k2 (k2=n−q+1) ofthe open circuit voltage change amount Dvoc and k2 of the differencegradient change amount Dsd, between the past time t_(q) and the presenttime t_(n), which corresponds to the period to the present from the pastthat goes back the predetermined time T4 from the present. Then, theinput data generation unit 132 uses the extracted data sets of Dvoc andDsd (Dvoc, Dsd) each time to calculate the second gradient change rateR2, which is the change rate of the difference gradient change amountDsd with respect to the open circuit voltage change amount Dvoc, by theleast squares method.

More specifically, as shown in FIG. 9, the k2 of data sets (Dvoc, Dsd)is plotted on a two-dimensional plane having the open circuit voltagechange amount Dvoc as the horizontal axis and the difference gradientchange amount Dsd as the vertical axis. The plot is represented by ablack circle in the illustrated dotted ellipse. The slope of theapproximate straight line (regression straight line) 204 of this plotcorresponds to the second gradient change rate R2. In other words, whenthe approximate straight line 204 is given by Dsd=a3×Dvoc+b3, the slopea3 of the approximate straight line 204 corresponds to the secondgradient change rate R2. Here, the approximate straight line iscalculated by, for example, the least squares method.

[2.2.2.6. Voltage Estimation Input Data]

As described above, the voltage estimation input data is configured oftime-series data of the terminal currents Ite, the terminal voltagesVte, and the difference gradients Sdiff in the period to the presentfrom the past that goes back the predetermined time T2 from the present.Assuming that the present time is t_(n) and the past time that goes backthe predetermined time T2 from the present is t_(r), the voltageestimation input data is expressed by the following expression.

$\begin{matrix}\left\lbrack {{Expression}2} \right\rbrack &  \\{{{\,^{V}x}1\left( t_{n} \right)} = {\begin{pmatrix}{{\,^{V}{Ite}}\left( t_{n} \right)} \\{{\,^{V}{Vte}}\left( t_{n} \right)} \\{{\,^{V}{Sdiff}}\left( t_{n} \right)}\end{pmatrix}{where}}} & (1)\end{matrix}$ ^(V)Ite(t_(n)) = (Ite(t_(r)), Ite(t_(r + 1)), Ite(t_(r + 2)), …, Ite(t_(n))) ^(V)Vte(t_(n)) = (Vte(t_(r)), Vte(t_(r + 1)), Vte(t_(r + 2)), …, Vte(t_(n))) ^(V)Sdiff(t_(n)) = (Sdiff(t_(r)), Sdiff(t_(r + 1)), Sdiff(t_(r + 2)), …, Sdiff(t_(n))).

Here, the time-series data ^(v)Ite(t_(n)) of the terminal currents Ite,the time-series data ^(v)Vte(t_(n)) of the terminal voltages Vte, andthe time-series data ^(v)Sdiff(t_(n)) of the difference gradients Sdiffare respectively first-order tensors having n−r+1 values of terminalcurrent Ite, terminal voltage Vte, and difference gradient Sdiff fromtime t_(r) to time t_(n) as elements. Therefore, the voltage estimationinput data ^(v)x1(t_(n)) is a second-order tensor.

[2.2.2.7. State Estimation Input Data]

As described above, the state estimation input data is configured oftime-series data of the difference gradients Sdiff, the open circuitvoltages Voc, the first gradient change rates R1, and the secondgradient change rates R2, in the period to the present from the pastthat goes back the predetermined time T5 from the present. Assuming thatthe present time is t_(n) and the past time that goes back thepredetermined time T5 from the present is t_(s), the state estimationinput data is expressed by the following expression.

$\begin{matrix}\left\lbrack {{Expression}3} \right\rbrack &  \\{{{\,^{V}x}2\left( t_{n} \right)} = {\begin{pmatrix}{{\,^{V}{Sdiff}}\left( t_{n} \right)} \\{{\,^{V}{Voc}}\left( t_{n} \right)} \\{{\,^{V}R}1\left( t_{n} \right)} \\{{\,^{V}R}2\left( t_{n} \right)}\end{pmatrix}{where}}} & (2)\end{matrix}$ ^(V)Sdiff(t_(n)) = (Sdiff(t_(s)), Sdiff(t_(s + 1)), Sdiff(t_(s + 2)), …, Sdiff(t_(n))) ^(V)Voc(t_(n)) = (Voc(t_(s)), Voc(t_(s + 1)), Voc(t_(s + 2)), …, Voc(t_(n))) ^(V)R1(t_(n)) = (R1(t_(s)), R1(t_(s + 1)), R1(t_(s + 2)), …, R1(t_(n))) ^(V)R2(t_(n)) = (R2(t_(s)), R2(t_(s + 1)), R2(t_(s + 2)), …, R2(t_(n))).

Here, the time-series data ^(v)Sdiff(t_(n)) of the difference gradientsSdiff, the time-series data ^(v)Voc(t_(n)) of the open circuit voltagesVoc, the time-series data ^(v)R1(t _(n)) of the first gradient changerates R1, and the time-series data ^(v)R2(t _(n)) of the second gradientchange rates R2 are respectively first-order tensors of the differencegradient Sdiff, the open circuit voltage Voc, the first gradient changerate R1 and the second gradient change rate R2, each having n−s+1 valuesfrom time t_(s) to time t_(b) as elements. Therefore, the stateestimation input data ^(v)x2(t _(n)) is a second-order tensor.

[2.3. Functions of Model Learning Unit]

The model learning unit 134 generates an open circuit voltage estimationmodel 124 by machine learning. In addition, the model learning unit 134executes the step S104 shown in FIG. 1 to train the state estimationmodel 126 by machine learning.

[2.3.1. Generation of Open Circuit Voltage Estimation Model]

The model learning unit 134 uses the voltage estimation input data(described above) generated by the input data generation unit 132, andthereby generates the open circuit voltage estimation model 124 bymachine learning. At that time, the model learning unit 134 acquires,for example, the time-series data of the open circuit voltages Voc ofthe secondary battery 102 from the learning management device 112, andperforms the machine learning using the acquired time-series data of theopen circuit voltages Voc as teacher data.

FIG. 10 is a diagram showing the configuration of the open circuitvoltage estimation model 124 generated by the model learning unit 134.The open circuit voltage estimation model 124 is configured of a neuralnetwork and has an input layer 300, an intermediate layer 302, and anoutput layer 304. The open circuit voltage estimation model 124 is, forexample, an RNN (Recurrent Neural Network).

The input layer 300 receives the voltage estimation input data of thesecond-order tensor represented by the above expression (1). In thisembodiment, the intermediate layer 302 includes an LSTM (Long Short TermMemory) configured in multiple layers. However, the intermediate layer302 is not limited to the LSTM. For example, the intermediate layer 302may be configured of a GRU (Gated Recurrent Unit).

The output layer 304 outputs the estimated value of the open circuitvoltage Voc at the time t₁ of the secondary battery 102 as the outputy1(t_(n)). In other words, the output y1(t_(n)) is an open circuitvoltage Voc(t_(n)) as a scalar quantity.

[2.3.2. Generation of State Estimation Model]

The model learning unit 134 executes the step S104 shown in FIG. 1 totrain the state estimation model 126 by machine learning using the stateestimation input data generated by the input data generation unit 132.At that time, the model learning unit 134 acquires, for example, thetime-series data of the SOCs and SOHs of the secondary battery 102calculated by the learning management device 112, and uses the acquiredtime-series data of the SOCs and SOHs as teacher data to perform theabove machine learning.

FIG. 11 is a diagram showing a configuration of the state estimationmodel 126 generated by the model learning unit 134. The state estimationmodel 126 is configured of a neural network having an input layer 310,an intermediate layer 312, and an output layer 314. For example, thestate estimation model 126 is an RNN.

The input layer 310 receives the state estimation input data of thesecond-order tensor represented by the above expression (2). Theintermediate layer 312 includes an LSTM configured in multiple layers inthis embodiment. However, the intermediate layer 312 is not limited toLSTM. For example, the intermediate layer 312 may be configured of GRU.

The output layer 314 outputs the estimated value of SOC and theestimated value of SOH at the time t_(n) of the secondary battery 102 asthe output ^(v)y2(t _(n)). In other words, the output ^(v)y2(t _(n)) isa first-order tensor whose elements are SOC(t_(n)) and SOH(t_(n)), whichare scalar quantities.

The open circuit voltage estimation model 124 and the state estimationmodel 126 generated as described above do not take input of the terminalcurrent Ite and terminal voltage Vte of the secondary battery as theyare, and takes input of the difference gradient Sdiff, which is thechange gradient of the voltage difference δVte calculated from thetime-series data of the terminal voltages Vte, with respect to thecurrent difference δIte calculated from the time-series data of theterminal currents Ite. In other words, the open circuit voltageestimation model 124 and the state estimation model 126 do not learn therelationship of the change mode of the terminal current Ite and theterminal voltage Vte themselves with the SOC and the like, and learn thechange mode of the change of the terminal current Ite and the terminalvoltage Vte, that is, the relationship of the high-order change modewith the SOC and the like.

As described above, such a high-order change mode of the terminalcurrent and the terminal voltage has a correlation with the internalstate of the secondary battery, at least among the secondary batteriesin the same type (for example, the secondary batteries in “lithium ionbattery” that are identical as the type). Therefore, the open circuitvoltage estimation model 124 and the state estimation model 126generated as described above can accurately estimate open circuitvoltages of secondary batteries with various electrical characteristicsof different manufacturers and models, and charge rates (SOC) and/ordeterioration degrees (SOH) thereof in operation of these secondarybatteries.

[3. Secondary Battery Used for Model Learning]

The secondary batteries 102 used for the model learning are desirably aplurality of various secondary batteries having different manufacturersand models, and different electrical characteristics. This can generatean open circuit voltage estimation model 124 and a state estimationmodel 126 in which the estimation accuracy does not change much formanufacturers and models. For example, in training the open circuitvoltage estimation model 124 and the state estimation model 126, it isdesirable to use a plurality of secondary batteries having differentelectrical characteristics such as SOC-OCV characteristics, SOC-internalimpedance characteristics, and/or their SOH dependence.

[4. Operation Mode of Secondary Battery in Model Learning]

The operation mode (charge-discharge story) of the secondary battery inmodel learning is desirably not only monotonously discharging orcharging between a fully charged state (SOC=100%) and a fully dischargedstate (SOC=0%), but also randomly charging and discharging, and/oralternately charging and discharging according to predeterminedcriteria. Such predetermined criteria can be a standard according to theuse of the secondary battery to be estimated. For example, when asecondary battery for a vehicle is to be estimated, the criteria to beused can adjust to follow the typical charge-discharge cycle in vehicledriving in various traffic scenes such as urban areas, mountainousareas, rural areas, and highways.

[5. Collection of Learning Data]

In this embodiment, the state variables (Ite, Vte) of the secondarybattery 102, which is the source of the learning data of the opencircuit voltage estimation model 124 and the state estimation model 126,and the time-series data of SOCs and SOHs, which are teacher data, areacquired from the characteristic measuring instrument 110 by the machinelearning device 100, and are calculated by the learning managementdevice 112 and immediately used for training the open circuit voltageestimation model 124 or the state estimation model 126. However, thetime-series data of these state variables and teacher data do notnecessarily need to be used immediately for learning.

The time-series data of the state variables and the time-series data ofthe teacher data may be acquired and stored in advance by the learningmanagement device 112 operating the secondary battery. The machinelearning device 100 may acquire the time-series data of the statevariables and the time-series data of the teacher data stored in thelearning management device 112 from the learning management device 112,and train the open circuit voltage estimation model 124 and the stateestimation model 126.

In addition, the time-series data of the state variables and thetime-series data of the teacher data may be generated by a computersimulating the charge-discharge characteristics obtained from the designdata such as the equivalent circuit of the secondary battery 102, aslong as the error from the actual data is within a range that has nopractical problem.

[6. Example of State Estimation by State Estimation Model]

The following describes an example of state estimation of the secondarybattery using the trained state estimation model by the learning methodaccording to this embodiment. FIG. 12 is a diagram showing an example ofstate estimation of a secondary battery performed using a trained opencircuit voltage estimation model and a state estimation model.

The training data for the open circuit voltage estimation model and thestate estimation model are both generated by a computer simulating thecharge-discharge characteristics of sample secondary batteries, forvehicles, in dozens of types with different electrical characteristics.Specifically, the above computer simulation calculates the terminalcurrent Ite and terminal voltage Vte, and SOC and SOH at eachpredetermined time interval dt in charging and discharging according toa predetermined charge-discharge story, for each of the sample secondarybatteries in dozens of types with different electrical characteristics,which are SOC-OCV characteristics, internal impedance characteristics,and capacitive characteristics (SOH).

The above charge-discharge story includes: not only monotonouslydischarging or charging the sample secondary batteries between a fullycharged state (SOC=100%) and a fully discharged state (SOC=0%); but alsoadjusting to follow the typical charge-discharge cycle in vehicledriving in various traffic scenes such as urban areas, mountainousareas, rural areas, and highways.

The sample secondary batteries are lithium ion batteries. Themeasurement interval dt of the state variable is 100 ms. Thepredetermined times T1, T2, T3, T4, and T5 in calculating the voltageestimation input data and the state estimation input data of the opencircuit voltage estimation model and the state estimation modeldescribed above are respectively 5 seconds, 5 seconds, 300 seconds, 200seconds, and 5 seconds. Note that the numerical values of these timesare an example, and the predetermined times T1, T2, T3, T4, and T5 maybe set to different time values from the above.

FIG. 12 shows the estimation result of SOC and SOH using the trainedstate estimation model and the simulated value of SOC and SOH of onesecondary battery, randomly chosen from the above sample secondarybatteries to be the secondary battery to be estimated (hereinafter, thetarget secondary battery), in discharging the target secondary batteryfrom the fully charged state to the fully discharged state.

In FIG. 12, the horizontal axis is the elapsed time after the start ofdischarging when the secondary battery starts discharging from the fullycharged state, and the vertical axis is SOC (%) and SOH (Ah) of thetarget secondary battery. The state estimation input data given to thestate estimation model in the estimation of SOC and SOH is calculatedbased on Ite and Vte for each predetermined time interval dt at the timeof discharging the target secondary battery, which is calculated fromthe charge-discharge characteristics of the target secondary battery bysimulation.

In FIG. 12, lines 600 and 602 each formed by a set of gray dots arerespectively the SOC estimated values and the SOH estimated valuesestimated by the state estimation model. Lines 604 and 606 each formedby a set of black dots are respectively SOC and SOH calculated bysimulation from the charge-discharge characteristics of the targetsecondary battery.

The contrast between the line 600 and the line 604 and the contrastbetween the line 602 and the line 606 shown in FIG. 12 shows that thestate estimation model trained by the learning method shown in thisembodiment accurately estimates the SOC and SOH of the target secondarybattery. In particular, although the state estimation model used forthis estimation is generated using learning data for the samplesecondary batteries in dozens of types with different electricalcharacteristics, the estimated values of SOC and SOH each focuses on oneline (line 600 and line 602) without divergence, and accuratelyestimates the SOC and SOH for a specific target secondary battery. Thisindicates that the state estimation method of this embodiment, whichperforms learning using a plurality of secondary batteries withdifferent electrical characteristics, can accurately estimate the state,that is, SOC and SOH of a variety of operating secondary batterieshaving different manufacturers and models.

Second Embodiment

The following describes a second embodiment of the present invention.FIG. 13 is a diagram showing a procedure of a secondary battery stateestimation method according to an embodiment of the present invention.This state estimation method includes: a step (S300) of measuring statevariables including terminal currents and terminal voltages of anoperating secondary battery to which a load or a charger is connected,at predetermined time intervals; and a step (S302) of preprocessing themeasured state variables to calculate state estimation input data. Inaddition, this state estimation method includes a step (S304) ofestimating a state of the operating secondary battery from the stateestimation input data, using the state estimation model trained by thelearning method according to the first embodiment described above. Theabove states are SOC and SOH in this embodiment.

The state estimation method shown in FIG. 13 is executed, for example,in a state estimation device 400 shown in FIG. 14. The state estimationdevice 400 estimates the state of the secondary battery 404, which ismounted on a vehicle 402 that is an electric vehicle and is operating asan in-vehicle battery of the vehicle 402, for example. The secondarybattery 404 is connected to a rotary electric machine 410 via acharacteristic measuring instrument 406 and an energization controller408.

The rotary electric machine 410 functions as a motor that is powered bythe discharge from the secondary battery 404 to drive the wheels of thevehicle 402, and also functions as a generator that generateselectricity by the rotational force transmitted from the wheels andcharges the secondary battery 404.

The characteristic measuring instrument 406 measures the present valueof the state variable including the terminal current Ite and theterminal voltage Vte of the secondary battery 404. The energizationcontroller 408 controls the amount of electricity from the secondarybattery 404 to the rotary electric machine 410 and the amount ofelectricity from the rotary electric machine 410 to the secondarybattery 404 under the control of a driving control device 414 mounted onthe vehicle 402. When an external charging device 412 outside thevehicle 402 is connected to the vehicle 402, the energization controller408 controls the amount of electricity supplied from the externalcharging device 412 to the secondary battery 404 under the control ofthe driving control device 414. The external charging device 412 is, forexample, a charger in a charging stand. The energization controller 408can also control the amount of electricity from the generator to thesecondary battery when another generator driven by the internalcombustion engine is mounted on the vehicle 402.

The driving control device 414 acquires the estimated values of thepresent SOC and SOH indicating the state of the secondary battery 404from the state estimation device 400, and uses the acquired SOC and SOHto control the operation of the rotary electric machine 410 and notifythe user.

Specifically, the driving control device 414 has a processing device 440and a storage device 448. The storage device 448 is, for example, asemiconductor memory, and stores data necessary for processing in theprocessing device 440.

The processing device 440 is, for example, a computer including aprocessor such as a CPU. The processing device 440 may be configured toinclude a ROM in which a program is written, and a RAM for temporarilystoring data. The processing device 440 includes a motor control unit442, a charge control unit 444, and a notification control unit 446, asfunctional elements or functional units.

These functional elements included in the processing device 440 areembodied, for example, by the processing device 440, which is acomputer, executing a program. Note that the computer program can bestored in any computer-readable storage medium. Alternatively, all orpart of the functional elements included in the processing device 440may be configured by hardware including one or more electronic circuitcomponents.

The motor control unit 442 detects the amount of depression of theaccelerator pedal (not shown) of the vehicle 402 from the acceleratorpedal sensor 452. When the accelerator pedal is depressed, the drivingcontrol device 414 instructs the energization controller 408 to energizethe rotary electric machine 410 from the secondary battery 404, andoperates the rotary electric machine 410 as a motor to drive the vehicle402. Furthermore, driving control device 414 controls the rotation speedof the rotary electric machine 410 via the energization controller 408so that the speed of the vehicle 402 acquired from the vehicle speedsensor 456 is a speed corresponding to the amount of depression of theaccelerator pedal.

At that time, the motor control unit 442 uses the estimated value of thepresent SOC acquired from the state estimation device 400, to limit theupper limit value (maximum flowing current) of the current flowing fromthe secondary battery 404 to the rotary electric machine 410, forexample, when the vehicle 402 is accelerating or traveling at a constantspeed. In other words, for example, the motor control unit limits thetorque generated by the rotary electric machine 410 to limit thedischarge of the secondary battery 404. For that, the motor control unitdetermines the maximum flowing current so that the fuel efficiency (forexample, the mileage per 1 kWh) determined from the characteristics ofthe secondary battery 404 and the rotary electric machine 410 is notless than a predetermined value.

The charge control unit 444 determines whether the brake pedal (notshown) of the vehicle 402 is depressed by the brake pedal sensor 454.Then, when the brake pedal is depressed, the charge control unit 444instructs the motor control unit 442 to stop the energization from thesecondary battery 404 to the rotary electric machine 410. Then, thecharge control unit 444 instructs the energization controller 408 toenergize the secondary battery 404 from the rotary electric machine 410to operate the rotary electric machine 410 as a generator, and therebycharges the secondary battery 404 from the rotary electric machine 410,which is called regenerative braking operation.

Furthermore, when the external charging device 412 is connected to thevehicle 402, the charge control unit 444 controls the amount of powersupplied from the external charging device 412 to the secondary battery404 via the energization controller 408.

The notification control unit 446 uses the present SOC estimated valueand the SOH estimated value acquired from the state estimation device400, to make a predetermined display on the display device 450. Forexample, the notification control unit 446 simply displays the acquiredpresent SOC estimated value and SOH estimated value on the displaydevice 450. Alternatively, for example, the notification control unit446 displays a message, on the display device 450, suggesting that thedriver charge the vehicle 402 at the charging stand when the SOCestimated value falls below a predetermined value. Alternatively, forexample, the notification control unit 446 displays a message, on thedisplay device 450, suggesting the driver of the vehicle 402 to replacethe secondary battery 404 when the SOH estimated value falls below apredetermined value.

The state estimation device 400 executes the state estimation method ofthe secondary battery shown in FIG. 13 to estimate the SOC and SOH ofthe operating secondary battery 404, and outputs the present SOCestimated value and SOH estimated value to the driving control device414.

Specifically, the state estimation device 400 has a processing device420 and a storage device 428. The storage device 428 is composed of anon-volatile and volatile semiconductor memory. The storage device 428stores the open circuit voltage estimation model 124 and the stateestimation model 126 trained by the learning method shown in the firstembodiment, in advance, as the open circuit voltage estimation model 430and the state estimation model 432, respectively.

The processing device 420 is, for example, a computer including aprocessor such as a CPU. The processing device 420 may be configured toinclude a ROM in which a program is written, and a RAM for temporarilystoring data. The processing device 420 includes a state observationunit 422, a preprocessing unit 424, and a state estimation unit 426 asfunctional elements or functional units.

These functional elements included in the processing device 420 areembodied, for example, by the processing device 420, which is acomputer, executing a program. Note that the computer program can bestored in any computer-readable storage medium. Alternatively, all orpart of the functional elements included in the processing device 420may be configured by hardware including one or more electronic circuitcomponents.

FIG. 15 shows a functional block diagram of the processing device 420having the state observation unit 422, the preprocessing unit 424, andthe state estimation unit 426. In FIG. 15, the dotted rectangles eachindicate processing in the preprocessing unit 424.

The state observation unit 422 executes the step S300 shown in FIG. 13.Specifically, the state observation unit 422 acquires the statevariables of the secondary battery 404 including the terminal currentsIte(t) and the terminal voltages Vte(t) of the operating secondarybattery 404, from the characteristic measuring instrument 406 atpredetermined time intervals. As a result, the state observation unit422 obtains time-series data of the state variables measured atpredetermined time intervals.

The preprocessing unit 424 executes the step S302 shown in FIG. 13.Specifically, the preprocessing unit 424 preprocesses the statevariables acquired by the state observation unit 422, and calculates thestate estimation input data for the state estimation model 432.Specifically, the preprocessing unit 424 uses the time-series data ofthe terminal currents Ite and the time-series data of the terminalvoltages Vte acquired by the state observation unit 422, to calculatethe current difference δIte, which is the difference of the terminalcurrents Ite, and the voltage difference δVte, which is the differenceof the terminal voltages Vte (process 500 shown in FIG. 15). Then, thepreprocessing unit 424 calculates the difference gradient Sdiff, whichis the change rate of the voltage difference δVte with respect to thecurrent difference δIte (process 502 shown in FIG. 15).

In addition, the preprocessing unit 424 input the time-series data ofeach of the terminal currents Ite, the terminal voltages Vte, and theabove-calculated Sdiff in the period to the present from the past thatgoes back a predetermined time T2 from the present, to the open circuitvoltage estimation model 430 (process 504 in FIG. 15). Then, thepreprocessing unit 424 calculates the estimated value of the presentopen circuit voltage Voc of the secondary battery 404 by the opencircuit voltage estimation model 430 (process 506 in FIG. 15).

Furthermore, the preprocessing unit 424 calculates the integratedcurrent value ΣIte, which is the sum of the terminal currents Iteacquired continuously in the period to the present from the past thatgoes back the predetermined time T3 from the present (process 508 inFIG. 15). Furthermore, the preprocessing unit 424 calculates thedifference gradient change amount Dsd obtained by subtracting thedifference gradient Sdiff in the past that goes back the predeterminedtime T3 from the present, from the present difference gradient Sdiff(process 510 in FIG. 15).

Then, the preprocessing unit 424 calculates the change rate of thedifference gradient change amounts Dsd with respect to the integratedcurrent values ΣIte in the period to the present from the past that goesback the predetermined time T4 from the present, as the first gradientchange rate R1 (process 512 in FIG. 15).

Furthermore, the preprocessing unit 424 subtracts the open circuitvoltage Voc in the past that goes back the predetermined time T3 fromthe present, from the present open circuit voltage Voc, to calculate theopen circuit voltage change amount Dvoc (process 514 in FIG. 15).

In addition, the preprocessing unit 424 calculates the change rate ofthe difference gradient change amount Dsd with respect to the opencircuit voltage change amount Dvoc in the period to the present from thepast that goes back the predetermined time T4 from the present, as thesecond gradient change rate R2 (process 516 in FIG. 15).

Then, the preprocessing unit 424 determines the time-series data foreach of the difference gradients Sdiff, the open circuit voltages Voc,the first gradient change rates R1, and the second gradient change ratesR2, in the period to the present from the past that goes back thepredetermined time T5 from the present, to be the state estimation inputdata of the state estimation model 432 (process 518 in FIG. 15).

Note that the preprocessing unit 424 has: specific methods ofcalculating the current difference δIte, the voltage difference δVte,difference gradient Sdiff, the open circuit voltage change amount Dvoc,the integrated current value ΣIte, the difference gradient change amountDsd, the first gradient change rate R1, and the second gradient changerate R2; and the configurations of the state estimation input data. Boththe methods of calculating and configurations are the same as thosedescribed in the first embodiment.

The state estimation unit 426 executes the step S304 shown in FIG. 13.Specifically, the state estimation unit 426 uses the state estimationinput data calculated by the preprocessing unit 424 to estimate SOC andSOH, as the present state of the secondary battery 404, by the trainedstate estimation model 432. The state estimation unit 426 outputs theestimated present SOC and SOH values to the driving control device 414,as the SOC estimated value and the SOH estimated value.

The present invention is not limited to the configuration of the aboveembodiments, and can be implemented in various aspects without departingfrom the gist thereof.

For example, the machine learning device 100 in the first embodiment andthe state estimation device 400 in the second embodiment described aboveuse the trained open circuit voltage estimation model to acquire theopen circuit voltage of the secondary battery required for learning orstate estimation of the state estimation model. However, it is notessential to use the open circuit voltage estimation model in theacquisition of the open circuit voltage of the operating secondarybattery in the first and second embodiments.

The open circuit voltage of the operating secondary battery may beacquired according to the prior art, for example, in the following way:an alternating current, which is a measurement signal, is input to thesecondary battery 102 to measure the internal impedance Z; and themeasured internal impedance Z, the present terminal current, terminalvoltage, and load impedance are used for calculating the open circuitvoltage of the operating secondary battery. Alternatively, the opencircuit voltage of the operating secondary battery may be acquired, forexample, in the following way: the temperature dependence of the typicalinternal impedance Z of the secondary battery and the presenttemperature are used for calculating the present internal impedance Z;and the calculated internal impedance Z and the present terminalcurrent, terminal voltage, and load impedance are used for calculatingthe open circuit voltage of the operating secondary battery.

Furthermore, in the state estimation method of the operating secondarybattery according to this embodiment, the state estimation model istrained to estimate both the SOC and the SOH of the secondary battery.However, the state estimation model may be trained to estimate only oneof the SOC or SOH.

Furthermore, in the first embodiment, the state estimation input data tothe state estimation model 126 are the time-series data of thefollowing: the difference gradients Sdiff, the open circuit voltagesVoc, the first gradient change rates R1, and the second gradient changerates R2. However, the state estimation input data may be only thetime-series data of the difference gradients Sdiff.

However, in order to accurately estimate SOC and SOH for a wider rangeof secondary batteries of different manufacturers and models, it isdesirable to use time-series data of the other three input variables(open circuit voltages Voc, first gradient change rates R1 and secondgradient change rates R2) as well, for enabling the learning of thedifference in SOC vs. OCV characteristics between secondary batteries ofdifferent models and the like.

Furthermore, the state estimation input data to the state estimationmodel 126 may additionally include the time-series data of terminalcurrents Ite in the period to the present from the past that goes backthe predetermined time T5 from the present. This can further improve theaccuracy of the state estimation of the secondary battery by the stateestimation model. Here, the time-series data of the terminal currentsIte is expressed by the following expression.

^(V)Ite(t _(n))=(Ite(t _(s)),Ite(t _(s+1)),Ite(t _(s+2)), . . . ,Ite(t_(n)))  [Expression 4]

In addition, in this embodiment, the current difference δIte and thevoltage difference δVte are respectively the fourth-order differenceΔ⁴Ite of terminal current and the fourth-order difference Δ⁴Vte ofterminal voltage. However, the current difference δIte and the voltagedifference δVte do not necessarily need to be the fourth-orderdifference. If the current difference δIte and the voltage differenceδVte are, for example, the first-order difference Δ¹Ite and Δ¹Vte, thestate estimation model 126 can learn the relationship of the behavior ofthe change (gradient) of the terminal current vs. the terminal voltagewith the SOC and SOH. However, a fourth-order or higher-order differencecan extract more common change modes of terminal current and terminalvoltage among secondary batteries with different electricalcharacteristics. Therefore, the fourth-order or higher-order differenceis preferable from the viewpoint of more accurately estimating SOC andSOH for secondary batteries of different manufacturers and models.

In addition, the input data of the open circuit voltage estimation model124 and the state estimation model 126 may additionally include thetime-series data of the temperatures of the secondary battery 102. Thiscan further improve the estimation accuracy of SOC and SOH.

In addition, in the above-described embodiment, the open circuit voltageestimation model 124 and the state estimation model 126 is an RNN, whicheasily handles continuous data in chronological order as input. However,the configuration of the open circuit voltage estimation model and thestate estimation model is not limited to the RNN.

For example, the open circuit voltage estimation model 124 and the stateestimation model 126 may both be configured by a one-dimensional CNN(Convolutional Neural Network). Also in this case, the voltageestimation input data and the state estimation input data (expressions(1) and (2)) expressed by the second-order tensor can be input to theopen circuit voltage estimation model 124 and the state estimation model126, respectively.

Furthermore, in the above-described embodiment, an example of the devicefor executing the step S304 of estimating the state of the operatingsecondary battery is the state estimation device 400 for estimating thestate of the operating secondary battery 404 that is mounted on thevehicle 402. However, the step S304 of estimating the state of theoperating secondary battery is not limited to the secondary battery forvehicles, and can be used for estimating the state of a secondarybattery used for any purpose such as a mobile phone, a bicycle, or ahome.

Furthermore, in the above-described embodiment, the state estimationdevice 400 is embodied as one integrated device that only performs stateestimation. However, this is only an example, and the step S304 ofestimating the state of the operating secondary battery can be executedin another device having a function other than the state estimation ofthe secondary battery. For example, the step S304 of estimating thestate of the operating secondary battery can be executed in thecontroller that controls the load of the secondary battery. As aspecific example, in FIG. 14, for example, the state observation unit422, the preprocessing unit 424, and the state estimation unit 426,which are included in the processing device 420 of the state estimationdevice 400, may be embodied by the processing device 440 of the drivingcontrol device 414. In this case, the open circuit voltage estimationmodel 430 and the state estimation model 432 stored in the storagedevice 428 are stored in the storage device 448 of the driving controldevice 414.

As described above, the learning method of the state estimation model ofthe secondary battery according to the first embodiment described aboveincludes the step S100. The step S100 measures state variables includingthe terminal currents Ite and the terminal voltages Vte of the operatingsecondary battery 102 to which the load 106 or the charger 104 isconnected, at each predetermined time interval dt. Furthermore, thislearning method includes the step S102 and the step S104. The step S102preprocesses the state variables to calculate state estimation inputdata. The step S104 trains the state estimation model 126 to learn therelationship of the state estimation input data with the charge rate SOCand/or deterioration degree SOH of the operating secondary battery 102,by machine learning. Then, the step S102 of calculating the stateestimation input data uses the time-series data of the terminal currentsIte and the time-series data of the terminal voltages Vte to calculate acurrent difference δIte, and a voltage difference δVte. The currentdifference δIte is the difference of the terminal currents Ite, and thevoltage difference δVte is the difference of the terminal voltages Vte.Furthermore, the step S102 uses the time-series data of the currentdifferences δIte and the time-series data of the voltage differencesδVte, to calculate the difference gradient Sdiff. The differencegradient Sdiff is the change rate of the voltage differences δVte withrespect to the current differences δIte, in the period to the presentfrom the past that goes back a first predetermined time T1 from thepresent. Then, the step S102 generates the state estimation input data^(v)x2(t _(n)) including the time-series data ^(v)Sdiff(t_(n)) of thedifference gradients Sdiff.

This configuration can generate a state estimation model that canaccurately estimate the state of charge rate and/or deterioration degreeof secondary batteries with various electrical characteristics ofdifferent manufacturers and models while the secondary batteries are inoperation.

Furthermore, the state estimation input data ^(v)x2(t_(n)) furtherincludes the time-series data ^(v)Voc(t_(n)) of the open circuitvoltages Voc, the time-series data ^(v)R1(t_(n)) of the first gradientchange rates R1, and the time-series data ^(v)R2(t_(n)) of the secondgradient change rates R2 of the operating secondary battery 404.

Here, the first gradient change rate R1 is determined as follows:

an integrated current value ΣIte is determined to be a sum of theterminal current values Ite acquired continuously in a period to apresent from a past that goes back a second predetermined time T3 fromthe present;

a difference gradient change amount Dsd is determined by subtracting thedifference gradient Sdiff in the past that goes back the secondpredetermined time T3 from the present, from the present differencegradient Sdiff;

time-series data of the integrated current values Ite and time-seriesdata of the difference gradient change amounts Dsd determines a changerate of the difference gradient change amounts Dsd with respect to theintegrated current values Ite; and

the first gradient change rate R1 is determined to be the change rate ina period to the present from a past that goes back a third predeterminedtime T4 from the present.

In addition, the second gradient change rate R2 is determined asfollows:

an open circuit voltage change amount Dvoc is determined by subtractingthe open circuit voltage Voc in the past that goes back the secondpredetermined time T3 from the present, from the present open circuitvoltage Voc;

time-series data of the open circuit voltage change amounts Dvoc andtime-series data of the difference gradient change amounts Dsddetermines a change rate of the difference gradient change amounts Dsdwith respect to the open circuit voltage change amounts Dvoc; and

the second gradient change rate R2 is determined to be the change ratein the period to the present from the past that goes back the thirdpredetermined time T4 from the present.

This configuration can improve the accuracy of estimating the chargerate and/or the deterioration degree of the secondary battery.

In addition, the state estimation input data ^(v)x2(t_(n)) furtherincludes time-series data ^(v)Ite(t_(n)) of the terminal currents Ite.This configuration can improve the estimation accuracy of the chargerate and/or the deterioration degree of the secondary battery by thegenerated state estimation model.

Furthermore, the difference gradient Sdiff, the first gradient changerate R1 and the second gradient change rate R2 are calculated by usingthe least squares method. This configuration can prevent a decrease inthe estimation accuracy of SOC and/or SOH due to a measurement error ofa state variable.

Furthermore, the step S102 of calculating the state estimation inputdata uses the time-series data ^(v)Ite(t_(n)) of the terminal currentsIte and the time-series data ^(v)Vte(t_(n)) of the terminal voltagesVte; and the time-series data ^(v)Sdiff(t_(n)) of the differencegradients Sdiff, as the voltage estimation input data, to estimate theopen circuit voltage Voc of the operating secondary battery 404. Then,the step S102 calculates the state estimation input data ^(v)x2(t _(n))using the above-estimated open circuit voltage Voc.

This configuration can accurately estimate the open circuit voltage of asecondary battery having various electrical characteristics of differentmanufacturers and models, and can further improve the estimationaccuracy of the charge rate and/or the deterioration degree of thesecondary battery of the state estimation model.

Furthermore, the step S102 of calculating the state estimation inputdata uses the open circuit voltage estimated using the trained opencircuit voltage estimation model 430, which has learned the relationshipbetween the voltage estimation input data and the open circuit voltageof the operating secondary battery, to calculate the state estimationinput data. This configuration can more accurately estimate the opencircuit voltage of the secondary battery having various electricalcharacteristics of different manufacturers and models, and can furtherimprove the estimation accuracy of the charge rate and/or thedeterioration degree of the secondary battery of the state estimationmodel.

The difference gradient Sdiff is calculated using the least squaresmethod. This configuration can prevent a decrease in the estimationaccuracy of SOC and/or SOH due to a measurement error of a statevariable in the generated state estimation model.

Furthermore, the current difference δIte and the voltage difference δVteare respectively the fourth-order difference Δ⁴Ite of the time-seriesdata of the terminal currents Ite and the fourth-order difference Δ⁴Vteof the time-series data of the terminal voltages Vte. According to thisconfiguration, the generated state estimation model uses higher-orderchange mode of the terminal currents Ite and terminal voltages Vte thatsecondary batteries with different electrical characteristics can havemore commonly, and thereby can more accurately estimate SOC and/or SOHof secondary batteries with various electrical characteristics ofdifferent manufacturers and models.

The state estimation model 126 is configured of an RNN or aone-dimensional CNN. The intermediate layer of the RNN configuring thestate estimation model 126 may be configured by an LSTM or a GRU. Thisconfiguration can efficiently handle the time-series data of a pluralityof variables and effectively train the state estimation model.

The state estimation model 126 is generated by learning usingtime-series data of state variables including the terminal currents Iteand the terminal voltages Vte for each of the plurality of secondarybatteries 102 having different electrical characteristics to which theload 106 or the charger 104 is connected. This configuration cangenerate a state estimation model capable of accurately estimating thestate of the charge rate and/or the deterioration degree of thesecondary battery having various electrical characteristics of differentmanufacturers and models.

The state estimation method of the secondary battery according to thesecond embodiment includes the step S300 of measuring state variablesincluding the terminal current Ite and the terminal voltage Vte of theoperating secondary battery 102 to which the load 106 or the charger 104is connected, at predetermined time intervals dt. This learning methodincludes: the step S302 of preprocessing the state variables andcalculating the state estimation input data; and the step S304 ofestimating the present charge rate and/or the deterioration degree ofthe operating secondary battery 102, from the state estimation inputdata, using the trained state estimation model 432 by the learningmethod according to the first embodiment. Then, the step S302 ofcalculating the state estimation input data uses the time-series data ofthe terminal currents Ite and the time-series data of the terminalvoltages Vte to calculate the current difference δIte, and the voltagedifference δVte. The current difference δIte is the difference of theterminal currents Ite, and the voltage difference δVte is the differenceof the terminal voltages Vte (process 500 in FIG. 15). The step S302uses the time-series data of the current differences δIte and thetime-series data of the voltage differences δVte, to calculate thedifference gradient Sdiff (process 502 in FIG. 15). The differencegradient Sdiff is the change rate of the voltage differences δVte withrespect to the current differences δIte, in the period to the presentfrom the past that goes back a first predetermined time T1 from thepresent. Then, the step S302 generates the state estimation input data^(v)x2(t_(n)) including the time-series data ^(v)Sdiff(t_(n)) of thedifference gradients Sdiff (process 518 in FIG. 15).

The state estimation method of the secondary battery according to thesecond embodiment is executed by, for example, the state estimationdevice 400. The state estimation device 400 includes a state observationunit 422 that measures state variables including the terminal currentIte and the terminal voltage Vte of the operating secondary battery 404at each predetermined time interval dt. In addition, the stateestimation device 400 includes a preprocessing unit 424 thatpreprocesses the state variables measured by the state observation unit422 and calculates the state estimation input data. Furthermore, thestate estimation device 400 includes a state estimation unit 426 thatuses the state estimation model 432 trained by the learning methodaccording to the first embodiment, to estimate the present charge rateand/or the deterioration degree of the operating secondary battery 102,from the state estimation input data.

Then, the preprocessing unit 424 uses the time-series data of theterminal currents Ite and the time-series data of the terminal voltagesVte acquired by the state observation unit 422, to calculates thecurrent difference δIte, and the voltage difference δVte. The currentdifference δIte is the difference in terminal currents, and the voltagedifference δVte is the difference in terminal voltages. In addition, thepreprocessing unit 424 uses the time-series data of the currentdifferences δIte and the time-series data of the voltage differencesδVte, to calculate the difference gradient Sdiff. The differencegradient Sdiff is the change rate of the voltage differences δVte withrespect to the current differences δIte, in the period to the presentfrom the past that goes back a first predetermined time T1 from thepresent. Then, the preprocessing unit 424 generates the state estimationinput data ^(v)x2(t _(n)) including the time-series data^(v)Sdiff(t_(n)) of the difference gradients Sdiff.

These configurations can accurately estimate the state of charge rateand/or deterioration degree of secondary batteries having variouselectrical characteristics of different manufacturers and models inoperation of these secondary batteries.

REFERENCE SIGNS LIST

-   -   100 . . . machine learning device, 102, 404 . . . secondary        battery, 104 . . . charger, 106 . . . load, 108 . . . changeover        switch, 110, 406 . . . characteristic measuring instrument, 112        . . . learning management device, 120, 420, 440 . . . processing        device, 122, 428, 448 . . . storage device, 124, 430 . . . open        circuit voltage estimation model, 126, 432 . . . state        estimation model, 130 . . . state variable measuring unit, 132 .        . . input data generation unit, 134 . . . model learning unit,        200, 202, 204 . . . approximate straight line, 300, 310 . . .        input layer, 302, 312 . . . intermediate layer, 304, 314 . . .        output layer, 400 . . . state estimation device, 402 . . .        vehicle, 408 . . . energization controller, 410 . . . rotary        electric machine, 412 . . . external charging device, 414 . . .        driving control device, 422 . . . state observation unit, 424 .        . . preprocessing unit, 426 . . . state estimation unit, 442 . .        . motor control unit, 444 . . . charge control unit, 446 . . .        notification control unit, 450 . . . display device, 452 . . .        accelerator pedal sensor, 454 . . . brake pedal sensor, 456 . .        . vehicle speed sensor, 500, 502, 504, 506, 508, 510, 512, 514,        516, 518 . . . process, 600, 602, 604, 606 . . . line

What is claimed is:
 1. A learning method of a state estimation model ofa secondary battery, the learning method using machine learning, thestate estimation model estimating a charge rate and/or a deteriorationdegree of the operating secondary battery, the secondary battery beingconnected to a load or a charger, the method comprising: a step ofmeasuring state variables at predetermined time intervals, the statevariables including terminal currents and terminal voltages of theoperating secondary battery; a step of calculating state estimationinput data by preprocessing the state variables; and a step of trainingthe state estimation model to learn a relationship of the stateestimation input data with the charge rate and/or the deteriorationdegree of the operating secondary battery, by machine learning, whereinthe step of calculating: uses time-series data of the terminal currentsand time-series data of the terminal voltages to calculate currentdifferences and voltage differences, each current difference being adifference in the terminal currents, each voltage difference being adifference in the terminal voltages; uses time-series data of thecurrent differences and time-series data of the voltage differences tocalculate difference gradients, each difference gradient being a changerate of the voltage differences with respect to the current differencesin a period to a present from a past that goes back a firstpredetermined time from the present; and generates the state estimationinput data including time-series data of the difference gradients. 2.The learning method of the state estimation model of the secondarybattery according to claim 1, wherein the state estimation input datafurther includes time-series data of open circuit voltages of theoperating secondary battery, time-series data of first gradient changerates, and time-series data of second gradient change rates, each firstgradient change rate being determined as follows: an integrated currentvalue is determined to be a sum of the terminal current values acquiredcontinuously in a period to a present from a past that goes back asecond predetermined time from the present; a difference gradient changeamount is determined by subtracting the difference gradient in the pastthat goes back the second predetermined time from the present, from thepresent difference gradient; time-series data of the integrated currentvalues and time-series data of the difference gradient change amountsdetermines a change rate of the difference gradient change amounts withrespect to the integrated current values; and the first gradient changerate is determined to be the change rate of the difference gradientchange amounts with respect to the integrated current values, in aperiod to the present from a past that goes back a third predeterminedtime from the present, each second gradient change rate being determinedas follows: an open circuit voltage change amount is determined bysubtracting the open circuit voltage in the past that goes back thesecond predetermined time from the present, from the present opencircuit voltage; time-series data of the open circuit voltage changeamounts and time-series data of the difference gradient change amountsdetermines a change rate of the difference gradient change amounts withrespect to the open circuit voltage change amounts; and the secondgradient change rate is determined to be the change rate of thedifference gradient change amounts with respect to the open circuitvoltage change amounts, in the period to the present from the past thatgoes back the third predetermined time from the present.
 3. The learningmethod of the state estimation model of the secondary battery accordingto claim 2, wherein each first gradient change rate and each secondgradient change rate are calculated using the least squares method. 4.The learning method of the state estimation model of the secondarybattery according to claim 1, wherein the step of calculating: usestime-series data of the terminal currents and time-series data of theterminal voltages, and time-series data of the difference gradients, asvoltage estimation input data, to estimate an open circuit voltage ofthe operating secondary battery; and uses the estimated open circuitvoltage to calculate the state estimation input data.
 5. The learningmethod of the state estimation model of the secondary battery accordingto claim 4, wherein the step of calculating uses the open circuitvoltage estimated using a trained open circuit voltage estimation model,and thereby calculates the state estimation input data, the trained opencircuit voltage estimation model having learned a relationship betweenthe voltage estimation input data and an open circuit voltage of theoperating secondary battery.
 6. The learning method of the stateestimation model of the secondary battery according to claim 1, whereineach difference gradient is calculated using the least squares method.7. The learning method of the state estimation model of the secondarybattery according to claim 1, wherein each current difference and eachvoltage difference are respectively a fourth-order difference oftime-series data of the terminal currents and a fourth-order differenceof time-series data of the terminal voltages.
 8. The learning method ofthe state estimation model of the secondary battery according to claim1, wherein the state estimation model is configured of an RNN (RecurrentNeural Network).
 9. The learning method of the state estimation model ofthe secondary battery according to claim 8, wherein an intermediatelayer of the RNN configuring the state estimation model is configured ofan LSTM (Long Short Term Memory) or a GRU (Gated Recurrent Unit). 10.The learning method of the state estimation model of the secondarybattery according to claim 1, wherein the state estimation model isconfigured of a one-dimensional CNN (Convolutional Neural Network). 11.The learning method of the state estimation model of the secondarybattery according to claim 1, wherein the state estimation model isgenerated by learning using time-series data of state variablesincluding terminal currents and terminal voltages for each of aplurality of secondary batteries with different electricalcharacteristics, the secondary batteries each being connected to a loador a charger.
 12. A state estimation method of a secondary battery, themethod comprising: a step of measuring state variables at predeterminedtime intervals, the state variables including terminal currents andterminal voltages of the operating secondary battery, the secondarybattery being connected to a load or a charger; a step of calculatingstate estimation input data by preprocessing the state variables; and astep of estimating a present charge rate and/or a present deteriorationdegree of the operating secondary battery, from the state estimationinput data, using the state estimation model trained by a learningmethod of the state estimation model of the secondary battery accordingto claim 1; wherein the step of calculating: uses time-series data ofthe terminal currents and time-series data of the terminal voltages tocalculate current differences and voltage differences, each currentdifference being a difference in the terminal currents, each voltagedifference being a difference in the terminal voltages; uses time-seriesdata of the current differences and time-series data of the voltagedifferences to calculate difference gradients, each difference gradientbeing a change rate of the voltage differences with respect to thecurrent differences in a period to a present from a past that goes backa first predetermined time from the present; and generates the stateestimation input data including time-series data of the differencegradients.
 13. A state estimation device of a secondary battery, thedevice comprising a processor, wherein the processor is configured to:measure state variables at predetermined time intervals, the statevariables including terminal currents and terminal voltages of anoperating secondary battery; perform preprocessing of the measured statevariables, to calculate state estimation input data; and estimate apresent charge rate and/or a present deterioration degree of theoperating secondary battery, from the state estimation input data, usinga state estimation model trained by the learning method of the stateestimation model of the secondary battery according to claim 1, whereinin the preprocessing, the processor: uses time-series data of themeasured terminal currents and time-series data of the measured terminalvoltages, to calculate current differences and voltage differences, eachcurrent difference being a difference in the terminal currents, eachvoltage difference being a difference in the terminal voltages; usestime-series data of the current differences and time-series data of thevoltage differences to calculate difference gradients, each differencegradient being a change rate of the voltage differences with respect tothe current differences in a period to a present from a past that goesback a first predetermined time from the present; and generates thestate estimation input data including time-series data of the differencegradients.